Kamvar, Sepandar D. and Klein, Dan and Manning, Christopher D. (2002) Interpreting and Extending Classical Agglomerative Clustering Algorithms using a Model-Based Approach. Technical Report. Stanford.
We present two results which arise from a model-based approach to hierarchical agglomerative clustering. First, we show formally that the common heuristic agglomerative clustering algorithms -- single-link, complete-link, group-average, and Ward's method -- are each equivalent to a hierarchical model-based method. This interpretation gives a theoretical explanation of the empirical behavior of these algorithms, as well as a principled approach to resolving practical issues, such as number of clusters or the choice of method. Second, we show how a model-based approach can be used to extend these basic agglomerative algorithms. We introduce adjusted complete-link, Mahalanobis-link, and line-link as variants of the classical agglomerative methods, and demonstrate their utility.
|Item Type:||Techreport (Technical Report)|
|Uncontrolled Keywords:||clustering, probabilistic models, model-based clustering, hierarchical clustering|
Computer Science > Data Mining
|Related URLs:||Project Homepage||http://www-nlp.stanford.edu/|
|Deposited By:||Import Account|
|Deposited On:||19 Feb 2002 16:00|
|Last Modified:||25 Dec 2008 09:35|
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